Thinking in Frequency Computer Vision Brown James Hays

Thinking in Frequency Computer Vision Brown James Hays Slides: Hoiem, Efros, and others

Recap of Wednesday linear filtering convolution differential filters filter types boundary conditions.

Review: questions 1. Write down a 3 x 3 filter that returns a positive value if the average value of the 4 -adjacent neighbors is less than the center and a negative value otherwise 2. Write down a filter that will compute the gradient in the x-direction: gradx(y, x) = im(y, x+1)-im(y, x) for each x, y Slide: Hoiem

Review: questions 3. Fill in the blanks: a) b) c) d) _ A F _ = = D _ D D * * B _ _ D Filtering Operator A B E G C F H I D Slide: Hoiem

Today’s Class • Fourier transform and frequency domain – Frequency view of filtering – Hybrid images – Sampling • Reminder: Read your textbook – Today’s lecture covers material in 3. 4 Slide: Hoiem

Why does the Gaussian give a nice smooth image, but the square filter give edgy artifacts? Gaussian Box filter

Hybrid Images • A. Oliva, A. Torralba, P. G. Schyns, “Hybrid Images, ” SIGGRAPH 2006

Why do we get different, distance-dependent interpretations of hybrid images? ? Slide: Hoiem

Why does a lower resolution image still make sense to us? What do we lose? Image: http: //www. flickr. com/photos/igorms/136916757/ Slide: Hoiem

Thinking in terms of frequency

Jean Baptiste Joseph Fourier (1768 -1830). . . the manner in which the author arrives at these equations is not exempt of difficulties and. . . his Any univariate function can beanalysis to integrate them still leaves something to be rewritten as a weighted sum of desired on the score of generality and even rigour. had crazy idea (1807): sines and cosines of different frequencies. • Don’t believe it? – Neither did Lagrange, Laplace, Poisson and other big wigs – Not translated into English until 1878! Laplace • But it’s (mostly) true! – called Fourier Series – there are some subtle restrictions Lagrange Legendre

A sum of sines Our building block: Add enough of them to get any signal g(x) you want!

Frequency Spectra • example : g(t) = sin(2πf t) + (1/3)sin(2π(3 f) t) = + Slides: Efros

Frequency Spectra

Frequency Spectra = = +

Frequency Spectra = = +

Frequency Spectra = = +

Frequency Spectra = = +

Frequency Spectra = = +

Frequency Spectra =

Example: Music • We think of music in terms of frequencies at different magnitudes Slide: Hoiem

Other signals • We can also think of all kinds of other signals the same way xkcd. com

Fourier analysis in images Intensity Image Fourier Image http: //sharp. bu. edu/~slehar/fourier. html#filtering

Signals can be composed + = http: //sharp. bu. edu/~slehar/fourier. html#filtering More: http: //www. cs. unm. edu/~brayer/vision/fourier. html

Fourier Transform • Fourier transform stores the magnitude and phase at each frequency – Magnitude encodes how much signal there is at a particular frequency – Phase encodes spatial information (indirectly) – For mathematical convenience, this is often notated in terms of real and complex numbers Amplitude: Phase:

The Convolution Theorem • The Fourier transform of the convolution of two functions is the product of their Fourier transforms • Convolution in spatial domain is equivalent to multiplication in frequency domain!

Filtering in spatial domain * = 1 0 -1 2 0 -2 1 0 -1

Filtering in frequency domain FFT = Inverse FFT Slide: Hoiem

Fourier Matlab demo

FFT in Matlab • Filtering with fft im = double(imread(‘…'))/255; im = rgb 2 gray(im); % “im” should be a gray-scale floating point image [imh, imw] = size(im); hs = 50; % filter half-size fil = fspecial('gaussian', hs*2+1, 10); fftsize = 1024; % should be order of 2 (for speed) and include im_fft = fft 2(im, fftsize); % 1) fil_fft = fft 2(fil, fftsize); % 2) image im_fil_fft = im_fft. * fil_fft; % 3) im_fil = ifft 2(im_fil_fft); % 4) im_fil = im_fil(1+hs: size(im, 1)+hs, 1+hs: size(im, 2)+hs); % 5) padding fft im with padding fft fil, pad to same size as multiply fft images inverse fft 2 remove padding • Displaying with fft figure(1), imagesc(log(abs(fftshift(im_fft)))), axis image, colormap jet Slide: Hoiem

Filtering Why does the Gaussian give a nice smooth image, but the square filter give edgy artifacts? Gaussian Box filter

Gaussian

Box Filter

Sampling Why does a lower resolution image still make sense to us? What do we lose? Image: http: //www. flickr. com/photos/igorms/136916757/

Subsampling by a factor of 2 Throw away every other row and column to create a 1/2 size image

Aliasing problem • 1 D example (sinewave): Source: S. Marschner

Aliasing problem • 1 D example (sinewave): Source: S. Marschner

Aliasing problem • Sub-sampling may be dangerous…. • Characteristic errors may appear: – “Wagon wheels rolling the wrong way in movies” – “Checkerboards disintegrate in ray tracing” – “Striped shirts look funny on color television” Source: D. Forsyth

Aliasing in video Slide by Steve Seitz

Aliasing in graphics Source: A. Efros

Sampling and aliasing

Nyquist-Shannon Sampling Theorem • When sampling a signal at discrete intervals, the sampling frequency must be 2 fmax • fmax = max frequency of the input signal • This will allows to reconstruct the original perfectly from the sampled version v v v good bad

Anti-aliasing Solutions: • Sample more often • Get rid of all frequencies that are greater than half the new sampling frequency – Will lose information – But it’s better than aliasing – Apply a smoothing filter

Algorithm for downsampling by factor of 2 1. Start with image(h, w) 2. Apply low-pass filter im_blur = imfilter(image, fspecial(‘gaussian’, 7, 1)) 3. Sample every other pixel im_small = im_blur(1: 2: end, 1: 2: end);

Anti-aliasing Forsyth and Ponce 2002

Subsampling without pre-filtering 1/2 1/4 (2 x zoom) 1/8 (4 x zoom) Slide by Steve Seitz

Subsampling with Gaussian pre-filtering Gaussian 1/2 G 1/4 G 1/8 Slide by Steve Seitz

Why do we get different, distance-dependent interpretations of hybrid images? ?

Salvador Dali invented Hybrid Images? Salvador Dali “Gala Contemplating the Mediterranean Sea, which at 30 meters becomes the portrait of Abraham Lincoln”, 1976

Clues from Human Perception • Early processing in humans filters for various orientations and scales of frequency • Perceptual cues in the mid-high frequencies dominate perception • When we see an image from far away, we are effectively subsampling it Early Visual Processing: Multi-scale edge and blob filters

Campbell-Robson contrast sensitivity curve

Hybrid Image in FFT Hybrid Image Low-passed Image High-passed Image

Perception Why do we get different, distance-dependent interpretations of hybrid images? ?

Things to Remember • Sometimes it makes sense to think of images and filtering in the frequency domain – Fourier analysis • Can be faster to filter using FFT for large images (N log. N vs. N 2 for autocorrelation) • Images are mostly smooth – Basis for compression • Remember to low-pass before sampling

Practice question 1. Match the spatial domain image to the Fourier magnitude image 1 2 3 4 5 B A C E D

Next class • Template matching • Image Pyramids • Filter banks and texture

Questions